Virtual reality tools and techniques for measuring cognitive ability and cognitive impairment

ABSTRACT

Techniques and tools for measuring cognitive ability and/or detecting cognitive impairment or decline. For example, techniques and tools are described that can be used to diagnose or test susceptibility to cognitive impairments in children or in elderly people (such as cognitive impairments associated with Alzheimer&#39;s Disease). Techniques and tools are described that can be used to evaluate treatment effects and/or measure cognitive decline over time.

RELATED APPLICATION INFORMATION

The present application claims the benefit of U.S. Provisional PatentApplication No. 60/928,577, entitled “Computer Software-Oriented Toolsand Techniques for Measuring Cognitive Ability and CognitiveImpairment,” filed May 9, 2007, the disclosure of which is incorporatedby reference.

GOVERNMENT SUPPORT

The inventions described in this patent application were made in part bygovernment support under NIH Grant # P30 AG08017. The United StatesGovernment may have rights in these inventions.

FIELD

The present disclosure relates to tools and techniques for measuringcognitive ability and/or impairment.

BACKGROUND

Many environmental and intrinsic factors influence cognitive function.Intrinsic factors that can influence cognitive function include sex, ageand genetic makeup.

Sex Differences in Cognitive Function

Effects of sex on cognitive function have been shown in humans andanimal models using established tests. Sex differences have beendemonstrated in both episodic memory tasks (favoring women) and spatialvisualization tasks (favoring men). Interestingly, in some studiesalcohol consumption abolished sex differences in spatial visualization,but not episodic memory performance. In addition, stress has been shownto differentially affect fear conditioning in men and women.

Consistent with the human studies, effects of sex on cognitive functionhave also been reported in animal models using established tests. Ingeneral, studies of spatial learning and memory in rodents have shownthat males learn more quickly than females and exhibit superiorperformance in a variety of mazes. Some studies, however, have not shownsuch differences between the sexes. Sex differences in classical fearconditioning and shuttlebox avoidance conditioning in rats have alsobeen reported. In addition, in some studies neonatal isolationfacilitated appetitive response learning in adult female, but not male,rats.

Cognitive tests administered to humans and animals frequently involvelarge differences. Therefore, it often remains difficult to directlycompare results on these tests across species. For example, whilespatial learning and memory can be easily assessed in humans and animalmodels, to compare assessments of spatial learning and memory in humansand mice, navigation to a target can be important. In some tests ofspatial memory, when all the information is within one field of view,the participant has an aerial perspective and a body-centered(egocentric) frame of reference (e.g. table-top tests of spatialmemory). Such tests are routinely used to assess visuospatial memory,but are very different from tests of spatial memory typically used forrodents. Testing visuospatial memory in rodents typically involves aviewer perspective of a world-centered (allocentric) frame of referencewith information found throughout a complex environment in which theparticipant has to navigate. Making direct inferences about performanceon navigation tests from performance on table-top tests can beproblematic.

Virtual reality (“VR”), which has been used to assess, expose, anddesensitize (in phobias) event and place-related memories, to assess andteach driving and flying skills, and to distract in pain management, canalso be used to assess spatial learning and memory in humans using anavigational task. Navigation in a virtual environment has been shown tobe sensitive to effects of sex of participants in some, but not all,studies. In one study, a virtual environment consisting of a series ofinterconnected hallways, some leading to dead ends and others leading toa designated goal location, was used to study age and sex differences inspatial navigation. In this study, there was no significant effect ofsex on time to complete the maze or total distance traveled, but therewas an effect of sex on total number of deviations from the correctroute into a dead-end corridor, and there was an effect of sex on howoften participants traveled on a portion of the correct route throughwhich they had already traveled. However, as there was no cued versionof this test, it is difficult to distinguish task learning performancefrom spatial learning and memory performance. In another study from thesame authors, a virtual water maze environment was used to study theeffects of age and sex on spatial learning and memory in humans. (Thewater maze paradigm is commonly used to assess spatial learning andmemory in rodents.) An effect of age, but not of sex, was detected onperformance. In this study, a trial with a visible target was givenfollowing the trials with a hidden target.

Apolipoprotein E (APOE) Genotype and Age Differences in CognitiveFunction

The three major human isoforms of apolipoprotein E (APOE), which areencoded by distinct APOE alleles (ε2, ε3, and ε4), are involved in themetabolism and redistribution of lipoproteins and cholesterol. Comparedwith ε2 and ε3, ε4 is associated with increased risk of cognitiveimpairments and of developing Alzheimer's disease (AD). Women are athigher risk to develop AD than men, particularly women carrying ε4. Incontrast to the risk to develop AD, the effects of ε4 on cognitivefunction in the non-demented elderly old-old (>75 years of age) are lessclear. While some studies have shown poor cognitive performance innon-demented elderly ε4 carriers compared with non-demented elderlynon-ε4 carriers and a small effect was observed in a meta-analysis,other studies did not.

In the elderly, high cortisol and low testosterone levels mightcontribute to reduced cognitive function. In older men and women, highercortisol levels have been associated with poorer cognitive performancein some studies. However, in another study cortisol levels onlyinversely correlated with paragraph recall in older participants withmild cognitive impairment (MCI) but not in elderly control participants.APOE genotype might also influence cortisol levels. In AD patients,higher cerebrospinal cortisol levels in ε4 than non-ε4 carriers havebeen reported, although comparable cerebrospinal cortisol in non-ε4 andε4 carriers have also been reported. In elderly men, low testosteronelevels might also contribute to reduced cognitive function. In oldermen, testosterone levels have been positively correlated with cognitivefunction, and cognitive function could be improved by testosteronetreatments. Similarly, testosterone, but not estrogen, levels in serumhave correlated positively with cognitive performance in older women,and androgen therapy has been shown to improve cognition in surgicallymenopausal women. The relationship between testosterone levels andcognitive function might be ε4-dependent. In men, low testosteronelevels and ε4 have been shown to interact in increasing the risk ofdeveloping AD. In addition, an interaction between ε4 and cognitiveperformance in healthy older men has been reported; while in non-ε4carriers higher testosterone levels were associated with better generalcognition, in ε4 carriers higher testosterone levels were associatedwith lower cognitive performance.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In summary, the Detailed Description is directed to various techniquesand tools for measuring cognitive ability and/or detecting cognitiveimpairment or decline. For example, techniques and tools are describedthat can be used to diagnose or test susceptibility to cognitiveimpairments in children or in elderly people (such as cognitiveimpairments associated with Alzheimer's Disease). Techniques and toolsare described that can be used to evaluate treatment effects and/ormeasure cognitive decline over time.

The foregoing and other objects, features, and advantages of theinvention will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a suitable computing environment inconjunction with which several described embodiments may be implemented.

FIG. 2 is a flowchart of a generalized technique for analysis ofcognitive status using VR testing.

FIG. 3 is a flowchart of a generalized technique for analysis ofcognitive status using Novel Image Novel Location (“NINL”) testing.

FIG. 4 is a diagram showing example panels of a NINL software toolaccording to one or more described embodiments.

FIG. 5 is a diagram showing screen shots of a virtual reality, spatialnavigation software tool according to one or more described embodiments.

FIGS. 6A and 6B are charts showing comparable facial recognition scoresin male and female participants, and correlation of the Faces I andFaces II scores, respectively.

FIGS. 7A-7F are charts showing NINL total scores of male and femaleparticipants, correlation of NINL I and NINL II, NINL scores for abilityto detect a change, NINL scores for ability to detect a novel image,NINL scores for ability to detect a novel location of a familiar image,and correlation of combined NINL total scores and combined facialrecognition total scores, respectively, according to one or moredescribed embodiments.

FIGS. 8A-8E are charts showing, for males and females tested with avirtual reality, spatial navigation software tool in hidden targettrials and visible target trials, results for latency to reach thetarget with (+) or without (−) wearing a head-mounted display (“HMD”),velocity, latency to reach the target, percentage time in the targetquadrant, percentage of successful trials, respectively, according toone or more described embodiments. FIG. 8F is a chart showing, for malesand females in a probe trial, percentage time in four quadrants,according to one or more described embodiments.

FIGS. 9A-9C are charts showing correlation between NINL total scores andlatency to reach the target during a visible target session with avirtual reality, spatial navigation software tool, correlation betweenNINL total scores and latency to reach the target during a hidden targetsession with a virtual reality, spatial navigation software tool, andcorrelation between NINL total scores and percentage of time spent inthe target quadrant during a probe trial, respectively, according to oneor more described embodiments.

FIGS. 10A-10D are charts showing, for elderly women and men, an effectof sex on “Family Pictures” test scores, effect of APOE ε4 on NINL totalscores (combined immediate and delayed scores), effect of APOE ε4 onNovel Location sub-scores (combined immediate and delayed scores), andeffect of sex on Novel Location sub-scores, respectively, according toone or more described embodiments.

FIGS. 11A and 11B are charts showing, for elderly women and men testedwith a virtual reality, spatial navigation software tool, effect of sexon velocity during visible target trials, and effect of sex on velocityduring hidden target trials, respectively, according to one or moredescribed embodiments.

FIGS. 12A-12F are charts showing, for elderly women and men tested witha virtual reality, spatial navigation software tool, effect of APOE ε4on velocity during a visible target session, effect of ε4 on velocityduring a hidden target session, effect of APOE ε4 on cumulative distanceduring a visible target session, effect of APOE ε4 on cumulativedistance during a hidden target session, effect of APOE ε4 on latency toreach target during a visible target session, and effect of APOE 64 onlatency to reach target during a hidden target session, respectively,according to one or more described embodiments.

FIGS. 13A and 13B are charts showing, for elderly women and men testedwith a virtual reality, spatial navigation software tool in a probetrial, effect of sex on percentage of time spent in four quadrants, andeffect of APOE ε4 on percentage of time spent in four quadrants,respectively, according to one or more described embodiments.

FIG. 14 is a chart showing, for elderly women and men, effect of ε4 onsalivary testosterone levels.

FIGS. 15A and 15B are charts showing, for elderly men, correlation ofsalivary cortisol levels with NINL I novel image recognition, andcorrelation of salivary cortisol levels with NINL II novel imagerecognition, respectively, according to one or more describedembodiments.

FIGS. 16A, 16B and 16C are charts showing effects of APOE ξ4 onperformance in 7-10 year-old boys and girls tested with a virtualreality, spatial navigation software tool, according to one or moredescribed embodiments.

DETAILED DESCRIPTION

Described embodiments are directed to techniques and tools for measuringcognitive ability and/or detecting cognitive impairment or decline. Forexample, techniques and tools are described that can be used to diagnoseor test susceptibility to cognitive impairments in children or inelderly people (such as cognitive impairments associated withAlzheimer's Disease). Techniques and tools are described that can beused to evaluate treatment effects and/or measure cognitive decline overtime. The various techniques and tools described herein may be usedindependently. Some of the described techniques and tools can be used incombination.

The following paragraphs include a discussion of terms used herein.

“Adjusting for effects of X” refers to adjusting an interpreted resultsuch that the condition X does not skew the interpreted result.

“Age-related cognitive decline” refers to a reduction in cognitionassociated with advancing age, e.g., an age-related dementia.

“Artificial intelligence” refers to information processing performed byone or more computers that mimics human reasoning.

“Based at least in part on X” means based on X and zero or more otheracts, results, or conditions.

“Cognitive status” refers to status of cognition-mental processesrelated to knowing, thinking, learning and/or judging.

“First-person” refers to a simulation of a perspective a user would haveif the user were physically present in a virtual environment.

“Learning of navigation skills” refers to gradual improvement ofnavigation skills through repetition, such as navigation skills used ina virtual reality environment.

“Learning of landscape” refers to gradual improvement of knowledge of alandscape, such as a landscape in a virtual reality environment.

“Measuring neural activity” refers to detecting activated brain regions.For example, neural activity can be measured using functional magneticresonance imaging (“fMRI”) techniques that measure changes inneuroanatomical activity, such as increased blood flow to areas of thebrain having corresponding neurological functions.

“No change score” refers to a performance signifier that measuresperformance of a user in identifying situations with no change in imagecontent or image location for a second set of one or more imagesrelative to a first set of one or more images.

“Novel image” refers to a new image in a second set of one or moreimages relative to a first set of one or more images.

“Novel location” refers to a new location of an image in a second set ofone or more images relative to a first set of one or more images.

“Pattern recognition” refers to identification of a pattern in data andassociation of the identified pattern with a condition or other data.

“Pediatric cognitive disability” refers to diminished cognition (ascompared to unaffected normal peers) in a human child under the age of18. Pediatric cognitive disability can be associated with, for example,a genetic abnormality or a neuropsychological disturbance.

“Pre-clinical Alzheimer's disease” refers to Alzheimer's disease in itsearly stages before memory disturbance significantly interferes withpsychosocial function to an extent that a clinical diagnosis can be madebased on the memory disturbance.

“Providing a treatment regimen” refers to setting or adjusting atreatment regimen, such as a dose of an anti-Alzheimer's diseasemedication or the degree to which an environment is structured toaddress the effects of dementia.

“Score” refers to a performance signifier, such as a number orpercentage of successful trials.

“User” refers to a human being that uses or interacts with computersoftware and/or a computerized system.

“Virtual reality environment” refers to an environment that simulates aphysical environment. For example, a computer can display a virtualreality environment to a user via a graphical display, and the user caninteract with the virtual reality environment by transmitting input tothe computer.

I. COMPUTING ENVIRONMENT

FIG. 1 illustrates a generalized example of a suitable computingenvironment (100) in which several of the described embodiments may beimplemented. The computing environment (100) is not intended to suggestany limitation as to scope of use or functionality, as the techniquesand tools may be implemented in diverse general-purpose orspecial-purpose computing environments.

With reference to FIG. 1, the computing environment (100) includes atleast one processing unit (110) and memory (120). In FIG. 1, this mostbasic configuration (130) is included within a dashed line. Theprocessing unit (110) executes computer-executable instructions and maybe a real or a virtual processor. In a multi-processing system, multipleprocessing units execute computer-executable instructions to increaseprocessing power. The memory (120) may be volatile memory (e.g.,registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flashmemory, etc.), or some combination of the two. The memory (120) storessoftware (180) implementing one or more of the described techniques andtools for testing cognitive ability and/or cognitive impairment.

A computing environment may have additional features. For example, thecomputing environment (100) includes storage (140), one or more inputdevices (150), one or more output devices (160), and one or morecommunication connections (170). An interconnection mechanism (notshown) such as a bus, controller, or network interconnects thecomponents of the computing environment (100). Typically, operatingsystem software (not shown) provides an operating environment for othersoftware executing in the computing environment (100), and coordinatesactivities of the components of the computing environment (100).

The storage (140) may be removable or non-removable, and includesmagnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, flashmemory, or any other medium which can be used to store information andwhich can be accessed within the computing environment (100). Thestorage (140) stores instructions for the software (180).

The input device(s) (150) may be a touch input device such as akeyboard, mouse, pen, touch screen, or trackball, a voice input device,a scanning device, or another device that provides input to thecomputing environment (100). For audio or video encoding, the inputdevice(s) (150) may be a sound card, video card, TV tuner card, orsimilar device that accepts audio or video input in analog or digitalform, or a CD-ROM, CD-RW or DVD that reads audio or video samples intothe computing environment (100). The output device(s) (160) may be adisplay, printer, speaker, CD- or DVD-writer, or another device thatprovides output from the computing environment (100).

The communication connection(s) (170) enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia include wired or wireless techniques implemented with anelectrical, optical, RF, infrared, acoustic, or other carrier.

The techniques and tools can be described in the general context ofcomputer-readable media. Computer-readable media are any available mediathat can be accessed within a computing environment. By way of example,and not limitation, with the computing environment (100),computer-readable media include memory (120), storage (140),communication media, and combinations of any of the above.

The techniques and tools can be described in the general context ofcomputer-executable instructions, such as those included in programmodules, being executed in a computing environment on one or more targetreal processors or virtual processors. Generally, program modulesinclude routines, programs, libraries, objects, classes, components,data structures, etc. that perform particular tasks or implementparticular abstract data types. The functionality of the program modulesmay be combined or split between program modules as desired in variousembodiments. Computer-executable instructions for program modules may beexecuted within a local or distributed computing environment.

II. GENERALIZED TECHNIQUE FOR ANALYSIS OF COGNITIVE STATUS USING VRTESTING

FIG. 2 shows a generalized technique (200) for analysis of cognitivestatus of a user using testing with a virtual reality (“VR”)environment. A software tool such as one operating in the computersystem environment shown in FIG. 1 or other tool performs the technique.Example use scenarios and example clinical applications for thegeneralized technique (200) are described below.

The tool receives (210) input from the user as the user interacts withsoftware presenting a VR environment. In example implementations(including those described in the following sections), the VRenvironment includes a first-person, three-dimensional graphicalrendering of the environment as well as sound cues for the environment.The environment is graphically rendered on a computer monitor or, for amore immersive experience, presented to the user using virtual realitygoggles or another head mounted display. Inputs (such as direction ofmovement, speed of movement) are received from the user using aforce-feedback joystick, other joystick, mouse, keyboard or other inputdevice.

Returning to FIG. 2, the tool measures (220) performance of the user inthe VR environment based at least in part upon the received input. Theorganization of the VR environment depends on implementation buttypically includes areas such as quadrants which may be organized interms of a coordinate space. As an intermediate part of measuringperformance, for example, the tool tracks position of the user over timein the coordinate space. The results of the tracking are stored inmemory or a file (e.g., as timestamped coordinate locations) for lateranalysis of patterns of user behavior.

In terms of metrics, the tool measures one or more of the following: (1)distance (e.g., cumulative or start to target) traversed in the VRenvironment, (2) time elapsed before reaching a target (or targets) inthe VR environment, (3) percentage of successful trials (where successis, e.g., finding a target), (4) time spent in a target area of the VRenvironment, (5) velocity of movement in the VR environment, (6) patternof movement (e.g., between multiple areas or in terms of coordinates) inthe VR environment, and/or (7) pattern of time spent in respective areasof the VR environment. Alternatively, the tool measures performanceusing other and/or additional metrics. Some metrics (such as velocity ofmovement and time elapsed before reaching a target) may depend on eachother to some extent, while other metrics do not.

In some implementations, the tool measures performance in a series of VRtests, with some tests having one or more “visible” targets and othertests having one or more “hidden” targets. In the “visible” targettrials, one or more visual or audible cues assist the user in finding atarget. For example, a prominent flag or other graphical cue is placednext to the target to help the user find the target, or directionalarrows guide the user to the target. In the hidden target trials, theperformance of the user in finding the target(s) is measured withoutgiving the user the cues from the visible target testing. Such testshelp measure memory retention of the user in navigating the VRenvironment.

Returning to FIG. 2, the tool uses (230) the measured performance of theuser in the VR environment in analysis of cognitive status. For example,the tool assesses: (a) the presence or extent of age-related cognitivedecline (e.g., a decline in memory performance or learning performance),(b) presence or extent of pediatric cognitive disability (e.g., a memoryperformance problem or learning performance problem), (c) presence orextent of progression of Alzheimer's disease, (d) presence of acharacteristic of pre-clinical Alzheimer's disease, and/or (e) responseof the user to therapeutic intervention to treat cognitive decline. Tomake the assessment, the tool can use artificial intelligence mechanismssuch as classifiers (e.g., neural networks) for pattern recognition,statistical analysis, etc. Example therapeutic interventions arepresented below. Alternatively, the tool uses the measured performancefor a different type of analysis.

The cognitive status assessment relates the measured performance to acognitive status classification. In making the assessment, the tool cancompensate for the effects of sex, age and/or learning about the VRenvironment (e.g., navigation skills, landscape) on the measuredperformance. The following sections describe observed correlationsbetween sex, age and learning in example uses of the generalizedtechnique (200), and such correlations can be compensated for during theassessment of cognitive status.

In some implementations, the user repeatedly takes the VR navigationtest and the performance of the user over time is measured so as toassess changes in cognitive status of the user. Typically, this involvescomparing cognitive status assessments from trial to trial for the user.In other implementations, the results of testing are compared formultiple users, e.g., as part of population studies for the efficacy ofa therapy.

III. GENERALIZED TECHNIQUE FOR ANALYSIS OF COGNITIVE STATUS USING NINLTesting

FIG. 3 shows a generalized technique (300) for using measuredperformance of a user on a Novel Image Novel Location (“NINL”) test inanalysis of cognitive status. A software tool such as one operating inthe computer system environment shown in FIG. 1 or other tool performsthe technique. Alternatively, the NINL test is administered by a humansupervisor using paper materials. Example use scenarios and exampleclinical applications for the generalized technique (300) are describedbelow.

The tool receives (310) input from the user as the user takes the NINLtest. In example implementations, a software tool graphically presentsthe NINL test to the user on a computer monitor as a series of images inpanels or a slideshow. The software tool accepts input from the user viaa keyboard, touchpad or mouse, or the software tool receives and processvoice input from the user, or the software tool receives and processesanother kind of input. In a typical NINL test, the input indicateswhether the user perceives “no change” in an image or group of imagespresented to the user, a “new location” in the image(s), or a “newimage” in the image(s). Alternatively, the choices presented to the userand/or selections received from the user have a different format.

Returning to FIG. 3, the tool measures (320) performance of the user inthe NINL test based at least in part upon the received input. In someimplementations, as described in the following sections, the toolpresents a first set of images organized by location to the user. Forexample, the first set of images includes multiple panels with eachpanel including multiple images. The tool then presents a second set ofimages organized by location to the user. For example, the second set ofimages includes multiple panels with each panel including multipleimages. To measure whether the user detects changes in image contentand/or location, one or more images of the second set of images differsin image content and/or image location relative to the first set ofimages. Alternatively, the sets of images for the NINL test have adifferent configuration.

In terms of metrics, the tool measures one or more of the following: (1)a novel location score indicating performance of the user in identifyingchanges in locations of images; (2) a novel image score indicatingperformance of the user in identifying new images in a second set of oneor more images relative to a first set of one or more images; and/or (3)a no change score indicating performance of the user in identifyingsituations with no change in image content or image location for asecond set of one or more images relative to a first set of one or moreimages. Alternatively, the tool measures performance using other and/oradditional metrics.

In some implementations, the tool measures performance in an “immediate”NINL test and also measures performance in a “delayed” NINL test. Forexample, the immediate NINL test occurs shortly after the user reviews atraining set of images for the NINL test, and the delayed NINL testoccurs some defined period (e.g., five minutes) after the immediate NINLtest. The duration of the defined delay period depends on implementationand is set to measure memory retention performance of the user.

Returning to FIG. 3, the tool uses (330) the measured performance of theuser on the test in analysis of cognitive status. For example, the toolassesses: (a) the presence or extent of age-related cognitive decline(e.g., a decline in memory performance or learning performance), (b)presence or extent of pediatric cognitive disability (e.g., a memoryperformance problem or learning performance problem), (c) presence orextent of progression of Alzheimer's disease, (d) presence of acharacteristic of pre-clinical Alzheimer's disease, and/or (e) responseof the user to therapeutic intervention to treat cognitive decline.Example therapeutic interventions are presented below. Alternatively,the tool assesses cognitive status for a different type of cognitiveassessment.

The cognitive status assessment relates the measured performance to acognitive status classification. In making the assessment, the tool cancompensate for the effects of sex, age and/or learning about the testingon the measured performance. The following sections describe observedcorrelations between sex, age and learning in example uses of thegeneralized technique (300), and such correlations can be compensatedfor during the assessment of cognitive status.

In some implementations, the user repeatedly takes the NINL test and theperformance of the user over time is measured so as to assess changes incognitive status of the user. Typically, this involves comparingcognitive status assessments from trial to trial for the user. In otherimplementations, the results of testing are compared for multiple users,e.g., as part of population studies for the efficacy of a therapy.

IV. EXAMPLE USE SCENARIOS

The generalized techniques (200, 300) can be used in various scenarios,including but not limited to home use scenarios, professional usescenarios with fMRI equipment, professional use scenarios with MRIequipment, and professional use scenarios with just the testing.

For example, when used with MRI equipment (or fMRI equipment), theequipment measures neural activity of the user as the user takes theNINL test and/or VR test. A cognitive status assessment for the user canthen also be based on the measured neural activity.

Or, when used in a home use scenario, the user takes the NINL testand/or the VR test on a home computer system such as a desktop or laptopcomputer. The test can be delivered to the user on a computer-readablemedium such as a disk or delivered to the user over a network connectionfrom a server computer system. The user inputs can be received andprocessed locally to measure performance and assess cognitive status, orinformation can be forwarded to a remote server computer site to measureperformance and/or assess cognitive status.

Alternatively, the NINL testing and/or VR testing is performed inconjunction with other and/or additional batteries of cognitive tests orphysical evaluations of the user.

Described implementations can be used in a variety of contexts, such aspsychological testing or clinical trials involving children and/or theelderly. The NINL object recognition test and the Memory Island spatialnavigation test turned out to be sensitive to detect differences inlearning and memory performance in these two populations. Thesensitivity of these tests is a potential benefit or advantage overother technology. For example, in contrast to established cognitivetests, these tests were shown to be sensitive to effects of APOE ε4 (arisk factor for developing Alzheimer's disease and cognitive impairmentsfollowing various environmental challenges) in non-demented elderly andchildren. Other advantages and problems to be solved are presentedherein.

V. EXAMPLE THERAPEUTIC INTERVENTIONS

In example implementations, cognitive status assessments are used tomake decisions about therapeutic interventions for users (e.g., childrenor elderly). In general, in this context, a therapeutic intervention isa known or proposed treatment for cognitive decline, such as thecognitive decline caused by normal aging or a pathological process, suchas Alzheimer's disease or another condition associated with dementia,such as a neurological disease (for example, Huntington's Disease,Parkinson's disease, Creutzfeldt-Jakob Disease or a brain tumor), avascular disorder (such as multi-infarct dementia or stroke), aninfectious etiology (such as HIV/AIDS, spongiform encephalopathy, orsyphilis), a toxic exposure (for example, to lead or alcohol), or anundesired effect of a drug. When the treatment is a proposed treatment,it can be administered as part of a clinical trial, and the response ofthe subject to the treatment can be assessed by the performance of thesubject in the VR environment and/or NINL testing.

For example, the therapeutic intervention includes a drug therapy, andthe cognitive status assessment is used in determining a therapeuticdose of the drug therapy. In the context of treating cognitive decline,for example, the therapeutic intervention is an APOE ε4 inhibitor, anAPOE ε3 or APOE ε2 mimetic, a cholinesterase inhibitor, anN-methyl-aspartate receptor antagonist, or a vitamin. Or, thetherapeutic intervention includes hormone therapy using testosteroneand/or another androgen, or using estrogen.

VI. EFFECTS OF SEX ON OBJECT RECOGNITION AND SPATIAL NAVIGATION INHUMANS

This section describes example implementations of NINL tests and VRspatial navigation tests, then details results of performance on thetests in a first series of trials. It includes discussion of specificproblems addressed and advantages for the example test implementationsin some contexts. Alternatively, implementations of the NINL and VRspatial navigation techniques and tools vary in terms of technicaldetails, specific advantages and/or problems solved.

A computer-generated VR island environment was developed to mirror thewater maze paradigm of spatial learning and memory sensitive to effectsof sex on age-related cognitive decline in mouse studies. Theparticipants were trained to navigate to a visible target andsubsequently to a hidden target. A joystick was used to controldirection and speed of body movement. In addition, potential effects ofsex on facial recognition and object recognition were tested using facesand NINL testing, respectively.

A. Materials and Methods

1. Participants

To determine the effects of sex on cognitive test performance, 27community college students between 20 and 44 years of age (mean age±S.E.M., 30.3±1.2 years of age; 14 males (mean age S.E.M., 29.1±1.4years of age) and 13 females (mean age ±S.E.M., 31.5±1.9 years of age))were tested.

To determine whether use of a head-mounted display (“HMD”) system (HMDmodel V8, Virtual Research System Inc., Santa Clara, Calif.) influencesperformance in a spatial learning and memory test requiring navigation(see below), 24 additional young participants between 17 and 40 years ofage (mean age ±S.E.M., 30.5±1.2 years of age; 14 males and 10 females)were recruited from the Oregon Health Science University campuscommunity.

2. Facial Recognition

The testing began with two non-computerized memory tests. The first ofthese was a facial recognition test (Faces I and Faces II), a part ofthe Wechsler Memory Scale III developed and published by thePsychological Corporation. In this test, the participant was shown aseries of 24 faces and asked to remember each one. Immediately after,the participant was shown another series of 48 faces (the 24 originalfaces plus 24 distracter faces) and asked to indicate whether each facewas one of the faces they were directed to remember earlier or not(Faces I score). After an interval of five minutes, the participant wasshown a different set of 48 faces (the same 24 original faces plus 24new distracter faces) and again asked to indicate whether each face wasone of the faces they were directed to remember earlier or not (Faces IIscore). For the Faces I and Faces II scores, the participant receivedone point for a correct response and zero points for an incorrectresponse with a maximal total of 48 points.

3. Object Recognition

Following the facial recognition test, an object recognition testentitled Novel

Image, Novel Location (“NINL”) test was presented to the studyparticipants. In this test, the participant was presented with a seriesof 12 panels, one at a time, for eight seconds each. Each panelconsisted of four quadrants (A, B, C, and D), with a different image inthree of the four quadrants. The images were all similar in complexitybut different in content. Positioning of the images within three of thefour quadrants varied between panels.

FIG. 4 is a diagram showing example panels of the NINL software tool. Onthe left are panels from the first set. On the right are thecorresponding panels from the second set, containing a novel location(A), novel image (B), or no change (C).

For each panel, the participant was asked to remember the images andtheir positions. After the participant had been presented the first setof 12 panels, they were immediately presented with a second set of 12panels. After five minutes, they were again presented the second set of12 panels. In the second set, the panels were either identical to, orslightly different from their counterparts in the first set. Thevariations in the new panels were either in the positioning of one ofthe three images (Novel Location) or in that they contained a novelimage in the location previously containing one of the three familiarimages (Novel Image). Out of the 12 panels in the second set, 4 panelswere identical to panels shown in the first set, four panels contained afamiliar image in a novel location, and four panels contained a novelimage in place of a familiar image. For each of the 12 panels in thesecond set, the participant was asked to identify the new panel as beingeither identical to the corresponding panel in the first set (“Yes”answer), or containing a novel image or a novel location of a familiarimage (“No” answer), with a maximum score of 12. These answers providedthe total score.

Test performance was also analyzed with four sub-scores. If a panel wasidentified as containing a novel image, the participant was asked whichimage on the panel was novel. If a panel was identified as containing anovel location of a familiar image, the participant was asked the novellocation of the familiar image on the panel.

The “No Change” sub-score (four points max) reflected correctidentification of panels identical to those seen in the first set. Nopoints were deducted for incorrect identification of a panel as beingidentical to one seen in the first set. The “Change” sub-score (eightpoints max) reflected the ability to identify and characterize the typeof anomaly in the panel (novel location or novel image), but not whetherthe particular image that changed was identified. No points were givenwhen a change was indicated but the type of anomaly (novel location ornovel image) was not correctly identified. The final two sub-scoresreflected the ability to correctly identify the exact Novel Location(maximal four points) or Novel Image (maximal four points). Inpreliminary studies involving study participants age-matched to those inthe current study, the version of the object recognition test describedworked well and did not lead to a ceiling effect in test performance asa result of being too easy.

4. Memory Island

Next, a computer-generated virtual reality world (Memory Island) wasused to assess spatial learning and memory. The participants wereimmersed in a computer-generated three-dimensional environment through aHMD system (HMD model V8, Virtual Research System Inc., Santa Clara,Calif.) comprised of special LCD video goggles and Sennheiserheadphones. Inside the visor of the helmet were two video screens, onefor each eye, generating a three-dimensional visual experience. Twoearphones presented stereo sounds that coincided with the visual imagesin the visor, further enhancing the immersion experience. A MicrosoftSidewinder joystick determined the direction and speed of movement inthe virtual world. As mentioned earlier, to determine whether use of theHMD influences performance in a spatial learning and memory testrequiring navigation, an additional cohort of participants was testedwith and without the HMD in two subsequent sessions using acounterbalanced design. Each session included four visible targettrials, four hidden target trials (target only visible in very closeproximity to the target), and a probe trial (no target present).Movement of the participant was tracked and recorded in time-stampedcoordinate files, which were used to calculate speed of movement, timeto reach the target (latency), and percentage time spent in eachquadrant during the visible target session and hidden target session.Percentage time spent in each quadrant is a valuable measure, as it isusually independent of velocity.

The virtual world simulated an island environment of 347 m×287 mcomprised of four quadrants. FIG. 5 is a diagram showing screen shots ofthe virtual reality, spatial navigation Memory Island software tool. Aflag marks the location of the target during the visible target session(A) while no flag is present during the hidden target session (B). Eachquadrant of the island has a different target. The target in quadrant 1is a fountain (C), in quadrant 2 a piece of moving art (D), in quadrant3 a seal (E), and in quadrant 4 a seagull (F).

As shown in FIG. 5, each quadrant contained a different target item. Theparticipant was first asked to navigate to a target location visiblymarked with a flag adjacent to the target (visible target). Targets inall four quadrants were used for visible target training in fourconsecutive trials. The starting orientation of the participant wasvaried in each trial, and these variations were kept consistent for allparticipants. As the starting orientation for a particular trialinfluenced the difficulty level of that trial, mean performance over thefour trials of the visible or hidden target session were used for dataanalysis rather than performance during individual trials. Aftertraining to locate the visible targets, the participant was trained tonavigate to a hidden target (here, no flag adjacent to the target, sothe participant had to remember where the hidden target was and how toget there). The location of the hidden target was kept constant for eachparticipant. Participants were given four trials with the hidden target.If the participant was unable to locate the target within two minutes,an arrow appeared to guide them to it. Trials in which the target waslocated within two minutes were defined as successful trials. Thepercentage of successful trials in the visible and hidden target sessionwas used as an additional performance measure. Following the hiddentarget trials, the participant received a thirty second probe trial(target removed).

5. Statistical Analysis

Statistical differences between groups were determined by ANOVA, withsex as between participant factor, followed by Tukey-Kramer post hoctests when appropriate. For analyzing probe trial data on Memory Island,the environment was divided into four quadrants and data was analyzedfor the percentage of time spent in each quadrant, with the percentageof time spent in each quadrant as a within-participant measure. Toassess significance of linear correlations, Pearson correlationcalculations with two-tailed p values were used. All these statisticswere performed using JMP software (SAS Institute Inc., Cary, N.C.).

B. Results of the First Series of Trials

human tests designed to mirror rodent tests of object recognition andspatial navigation were administered to adult cognitively healthyhumans. Facial recognition was also assessed. The trial results showedno statistically significant sex difference in facial recognition,consistent with earlier studies. In the object recognition test, thetest-retest NINL total scores during the same visit were highlycorrelated, comparable to the test-retest correlations obtained in theestablished facial recognition test. No statistically significanteffects were identified for sex on object recognition. However, in thespatial navigation test, effects were identified for sex on spatiallearning and memory during the session with the hidden, but not visible,target. These tests are useful to compare assessments of objectrecognition and spatial learning and memory in humans and animal models.

1. Facial Recognition Scores

First, facial recognition was assessed. FIGS. 6A and 6B are chartsshowing comparable facial recognition scores in male and femaleparticipants, and correlation of the Faces I and Faces II scores,respectively (n=14 males and n=13 females). No statistically significanteffect was identified for sex (F=0.5043, p=0.6810, FIG. 6A) on facialrecognition scores. The scores of Faces I and Faces II were highlycorrelated (r=0.8182, p<0.0001, FIG. 6B).

2. Novel Image and Novel Location (NINL)

Next, participants were tested for object recognition. FIGS. 7A-7F arecharts showing (A) NINL total scores of male and female participants,(B) correlation of NINL I and NINL II, (C) scores indicating ability todetect a change, (D) scores indicating ability to detect a novel image,(E) scores indicating ability to detect a novel location of a familiarimage, and (F) correlation of combined NINL total scores and combinedfacial recognition total scores, respectively (n=14 males and n=13females).

As with the facial recognition test, no statistically significant effectwas identified for sex on NINL total scores (F=0.5805, p=0.6305, FIG.7A). The scores of NINL trials 1 and 2 were highly correlated (r=0.8775,p<0.0001, FIG. 7B). Interestingly, the combined total scores for facialrecognition and NINL total scores were also highly correlated (r=0.5228,p<0.005, FIG. 7F).

With regard to the sub-scores, male and female participants showed nodifference in their ability to detect a change (F=0.3183, p=0.8121, FIG.7C), a novel image (F=0.6360, p=0.5952, FIG. 7D) or novel location(F=0.4148, p=0.7431, FIG. 7E).

3. Spatial Learning and Memory Requiring Navigation (Memory Island)

Finally, spatial learning and memory requiring navigation were assessedon Memory Island. FIGS. 8A-8E are charts showing, for males and femalestested with a virtual reality, spatial navigation software tool inhidden target and visible target trials, (A) results for latency toreach the target with (+) or without (−) wearing a HMD, (B) velocity,(C) latency to reach the target, (D) percentage time in the targetquadrant, (E) percentage of successful trials, respectively. FIG. 8F isa chart showing, for males and females in a probe trial, percentage timein four quadrants. In FIG. 8A, n=14 males and n=10 females for (A). InFIGS. 8B-8F, n=14 males and n=13 females.

The participants were first trained to locate a visible target in fourtrials (visible target session). Subsequently, they were trained tolocate a hidden target in four trials (hidden target session). Nostatistically significant effect was identified for the use of the HMDto perform this task on time to locate the target (latency) (F=0.92,p=0.5150, FIG. 8A), velocity (F=1.24, p=0.5300, FIG. 8B) or percentagetime spent in the target quadrant (F=2.14, p=0.323, FIG. 8D) during thevisible or hidden target session.

In both the visible target session and the hidden target session, thefemale participants moved slower (lower velocities) (F=15.59, p<0.0002,FIG. 8B) than the male participants. Analyzing the visible and hiddentarget sessions combined by repeated measures, the female participantsshowed higher latencies (F=19.22, p<0.0001, FIG. 8C) than the maleparticipants and there was a sex x session interaction (F=7.80,p=0.008). In the hidden target session, but not the visible targetsession, the females showed higher latencies than the males (FIG. 8C).As the female participants moved slower than the male participants inboth the visible and hidden target session and the magnitude of this sexdifference was comparable in the visible and the hidden target session(FIG. 8B), the sex difference in moving speeds did not account for thesex difference in latencies in the hidden target session (FIG. 8C).

Percentage time in the target quadrant, which is typically not affectedby velocity, was also measured. In the visible target session, femaleand male participants had no difficulty in locating the target and spentclose to 100% of their time searching in the target quadrant (FIG. 8D).In contrast, in the hidden target session, female participants spentless time in the target quadrant than male participants (F=12.27,p<0.001).

Additionally, the percentage of “successful” trials for each participantwas measured (FIG. 8E). A successful trial was defined as a trial inwhich the target was located within 120 seconds. Female participants hadfewer successful trials than male participants in the hidden targetsession (F=10.29, p=0.0021), but not the visible target session (F=1.94,p=0.2652).

Following the hidden target session, the participants performed a30-second probe trial in which there was no target present. Theparticipants were unaware of the absence of the target during the probetrial, and were asked to perform one last trial with the hidden target.Both females and males spent most of their time searching in the targetquadrant (FIG. 8F). There was a trend towards a sex difference with themales spending more time in the target quadrant than the females, butthat did not reach significance (F=3.49, p=0.0715).

4. Sex and Performance

Since there were sex differences in spatial navigation measures onMemory Island, these measures were examined for correlation withperformance on the other behavioral tests. FIGS. 9A-9C are chartsshowing correlation between NINL total scores and latency to reach thetarget during a visible target session with a virtual reality, spatialnavigation software tool, correlation between NINL total scores andlatency to reach the target during a hidden target session with avirtual reality, spatial navigation software tool, and correlationbetween NINL total scores and percentage of time spent in the targetquadrant during a probe trial, respectively.

In female, but not male, participants the combined NINL total scorescorrelated with average time to reach the target during the visibletarget session of Memory Island (r=−0.6736, p<0.01, FIG. 9A), withaverage time to reach the target during the hidden target session ofMemory Island (r=−0.6005, p<0.03, FIG. 9B), and with percentage of timespent in the target quadrant in the probe trial (r=0.7217, p<0.01, FIG.9C).

D. Discussion

In the object recognition test, the test-retest NINL total scores duringthe same visit were highly correlated, comparable to the test-retestcorrelations obtained in the established facial recognition test. In thespatial navigation test, effects were identified for sex on spatiallearning and memory during the session with the hidden, but not visible,target. No statistically significant effects were identified for sex onobject recognition.

There was a sex difference in the percentage of time in the targetquadrant during the hidden target, but not visible target, session. Thismeasure is independent of velocity and not biased by start location, asall participants started out in the center of the island. Therefore,these data tend to show a sex difference in ability to locate the hiddentarget per se, rather than in general ability to perform this taskregardless of whether the target was visible or hidden.

The identified sex differences in spatial learning and memory on MemoryIsland are consistent with sex differences in visual spatial perceptionand in spatial learning and memory in real and other virtual environmentnavigation tasks. Functional magnetic resonance imaging (“fMRI”) duringnavigational tasks has shown that women recruit the right parietal andright prefrontal area, whereas men recruit the left hippocampal area,which may relate to the predominant use of landmark cues by women andgeometric and landmark cues by men. However, it might be more complex.The Memory Island test environment contains landmarks predicting thetarget location and still showed sex differences in performance. Thesedata tend to show that the sex differences in spatial memory do notrequire the exclusion of stable landmarks.

The Memory Island test can be distinguished from prior studies that haveadapted the water maze test to study spatial learning and memory inhumans using VR. Programs designed to mirror the water maze test inrodent studies might lack elements found in real world situations.Compared to the prior studies, Memory Island involves a higher degree ofimmersion into the virtual environment. For example, Memory Island alsocontains environmental sounds (e.g., birds). While not a water mazeenvironment, the design and analysis of the water maze test wasincorporated into the design of Memory Island. Importantly, none of theparticipants experienced nausea or dizziness on Memory Island, while 10%of the participants experienced these symptoms after exposure to avirtual environment of interconnected hallways and other virtualenvironments in some prior studies.

In contrast to Memory Island, in NINL testing, no statisticallysignificant effects were identified for sex on object recognition.

These tests are useful in comparing assessments of object recognitionand spatial learning and memory in humans and animal models.

VII. EFFECTS OF SEX AND APOE ε4 ON OBJECT RECOGNITION AND SPATIALNAVIGATION IN THE ELDERLY

After describing example implementations of NINL tests and VR spatialnavigation tests, this section details results of performance on thetests in a second series of trials. It includes discussion of specificproblems addressed and advantages for the example test implementationsin some contexts. Alternatively, implementations of the NINL and VRspatial navigation techniques and tools vary in terms of technicaldetails, specific advantages and/or problems solved.

In the second series of trials using example implementations of NINLtests and VR tests, to determine effects of APOE ε4 (ε4) on cognitiveperformance of healthy elderly, 115 non-demented elders (mean age 81years) were cognitive tested. The established tests Faces, FamilyPictures, Spatial Span Forward and Backward, as well as the objectrecognition and spatial navigation tests described herein, were used ascognitive tests. Salivary samples were collected to determine APOEgenotype and salivary testosterone and cortisol levels.

Non-ε4- and ε4-carrying men and women did not differ in age, orMini-Mental State Examination, Wide Range Achievement Test-Reading, BeckAnxiety Inventory, or reaction time scores. In the second series oftrials, an effect was identified for ε4 on the object recognition andspatial navigation tests, however, with non-ε4 carriers outperforming ε4carriers, but not in the other cognitive tests. No relationship wasfound for sex and ε4 status or sex and performance during the hiddentarget session of Memory Island. In men, salivary cortisol levelscorrelated with object recognition. These results show that objectrecognition and spatial navigation tests are useful to assess cognitivefunction in the elderly.

A. Procedures

1. Study Participants

To determine the effects of sex and ε4 on cognitive performance in thenon-demented elderly, people ranging in age from 62 to 92 (mean age±S.E.M., 81.60±0.57 years) were tested. The inclusion criteria were: 1)age 55 and over; and 2) stable medical conditions. Exclusion criteriawere vision or hearing deficits severe enough to interfere withcognitive testing. Participants were given a Mini-Mental StateExamination (“MMSE”), a short questionnaire that tests different areasof cognitive function, with a maximum score of 30. (See Kurlowicz etal., “The Mini Mental State Examination (MMSE),” Try This: BestPractices in Nursing Care to Older Adults, Hartford Institute forGeriatric Nursing, no. 3 (January 1999).)

All participants had MMSE scores equal or greater than 22 (see below).

The final sample was composed of 115 participants, all whites. Thesample was divided into two APOE genotype groups, ε4 carriers and non-ε4carriers. Those in the non-ε4 carriers group represented ε3/ε3homozygotes and ε2/ε3 heterozygotes. Those in the ε4 carriers grouprepresented ε4/ε4 homozygotes, ε2/ε4, and ε3/ε4 heterozygotes (Table 1).

TABLE 1 APOE genotype distribution of study participants. Values arepresented as N (%) of women and men for each genotype. Genotype WomenMen ε2/ε3 13 (15.1%)  2 (6.9%) ε2/ε4  1 (1.2%)  0 (0.0%) ε3/ε3 52(59.3%) 22 (75.9%) ε3/ε4 18 (20.9)  5 (17.2%) ε4/ε4  2 (2.3%)  0 (0.0%)

The group of women consisted of 86 individuals (mean age ±S.E.M.,81.2±0.7 years of age), among them 65 non-ε4 carriers and 21 ε4carriers. The group of men consisted of 29 individuals (mean age±S.E.M., 82.9±0.9 years of age), among them 24 non-ε4 carriers and fiveε4 carriers. There was no significant sex difference in the proportionof ε4 carriers among men and women. When cognitive status of theparticipants was assessed using the MMSE, 111 participants had a MMSEscore greater than 23 which corresponds to a cutoff score forcognitively healthy people. The four participants who obtained a MMSEscore below 24 (three scored 23, one scored 22) performed well on theother cognitive tests. As MMSE scores can be affected by otherconditions such as hearing impairment, the data were analyzed with andwithout these four individuals included. Both analyses revealed asimilar pattern of results. Therefore, these four participants were notexcluded from the study.

Premorbid intellectual functioning general intelligence levels wereevaluated using the Wide Range Achievement Test-Reading (“WRAT-R”)instead of years of formal education. As anxiety levels and reactiontimes can influence performance on cognitive tests, they were analyzedas well. Levels of anxiety were assessed using the Beck AnxietyInventory (“BAI”). Reaction times were measured by presenting (on acomputer screen) a series of colored ellipses at varying time intervalsand asking the participants to press a button as soon as the ellipseappeared (Gary Darby, “Reaction Times,”http://www.delphiforfun.org/Programs/Reaction_times.htm (©2000-2007)).The amount of time between the appearance of the stimulus and the timethe button was pressed was recorded. No statistically significantdifferences were identified for age, cognitive status, pre-morbidintellectual functioning, anxiety levels or reaction times between menand women or non-ε4 and ε4-carrying study participants, respectively.(See Table 2.) The person testing the study participants was blinded toAPOE genotype.

TABLE 2 Demography of study participants. Values are presented as N (%)or adjusted mean ± S.E.M., as indicated. Sex ε4 Status Measure Women MenNon-ε4 ε4 Subjects 86 (74.8%) 29 (25.2%) 89 (77.4%) 26 (22.6%) [N (%)]Mean age 81.2 ± 0.7 82.9 ± 0.9 81.9 ± 6.4 80.5 ± 1.2 (years) WRAT-R 57.6± 1.0^(a,b) 57.5 ± 1.6^(c) 58.2 ± 1.0^(a,c) 55.3 ± 1.8^(b) MMSE 27.3 ±0.2 27.0 ± 0.3 27.3 ± 0.2 27.1 ± 0.4 BAI  4.2 ± 0.4^(a,b)  3.5 ± 0.8^(c) 4.2 ± 0.4^(a,c)  3.6 ± 0.7^(b) Reaction times^(d) 0.37 ± 0.01 0.40 ±0.02 0.38 ± 0.01 0.39 ± 0.02 ^(a)Two participants dropped out of thestudy between the two testing sessions. ^(b)Three participants droppedout of the study between the two testing sessions. ^(c)One scoremissing. ^(d)Two outliers removed from dataset.

2. Study Design

For APOE genotyping, samples of saliva were collected at the beginningof an evaluation session for a user. (See Table 3.)

TABLE 3 Sequence of cognitive testing. Test stage^(a) Description 1Collection of a saliva sample^(b) 2 Facial Recognition Immediate [FacesI] 5 min delay Face Recognition Delayed [Faces II] 3 NINL I 5 min delayNINL II 4 Reaction times 5 Memory Island: 3 Visible trials (threedifferent targets: Seagull Art Piece Seal) 3 Hidden trials (SeagullTarget) Probe trial 6 MMSE 7 FP I 5 min delay FP II 8 OraGene DNATest^(c) ^(a)Three months following this session, additional tests suchas BAI, the WAIS SSF and SSB, and the WRAT-R were conducted. These testswere not included in the first visit to minimize the length of the visitand potential fatigue. ^(b)Saliva samples were used to determinesalivary cortisol and testosterone levels. ^(c)OraGene tests were usedto determine APOE genotype.

The neuropsychological tests were administered in a designated apartmentof the retirement community in two visits lasting around two hours andone hour, respectively. Table 3 illustrates the sequence ofneuropsychological testing in both visits and the salivary collection.To control for circadian variations in hormone levels, all examineeswere tested in the morning starting at 8:30 a.m.

3. Cognitive Tests

a. Facial Recognition

For Faces I (immediate) and Faces II (delayed) tests (as in Section VI)to reduce the overall testing time and potential problems with fatigue,an interval of five minutes instead of 25 minutes was used between testand re-test for the facial recognition. A five-minute delay was alsoused for family pictures and object recognition tests. Performance onfacial recognition was analyzed using Faces I and Faces II scores.

b. Family Pictures

Family Pictures I (FP I, immediate) and Family Pictures II (FP II,delayed) are also part of the Wechsler Memory Scale III developed andpublished by the Psychological Corporation. In the FP tests, theparticipants were asked to memorize as many details as they could fromfour different cartoon-like family scenes. After the four scenes weredisplayed for 10 seconds each, the examinees were prompted to describewhich characters were in each scene, where they were positioned in thescene, and what they were doing. After an interval of five minutes, thestudy participants were asked the same question as in FP I. Each correctanswer was scored as one point. Performance was analyzed using FP I andFP II scores.

C. Object Recognition

The example implementation of Novel Image Novel Location (“NINL”) testdescribed in Section VI was used in the second series of trials.Briefly, this example NINL test consists of two sets of three-imagepanels (12 panels in each set). The three pictures on each panel aresimilar in complexity but different in content and how they are randomlylocated in three of the four quadrants of the panel (A, B, C, and D).(See FIG. 1.) The first set of 12 panels is the reference set (here, theset participants are asked to memorize). The second set is the test set;panels are either identical to, or slightly different from, theircounterparts in the first set. The variations in the second set ofpanels are either in the positioning of one of the three images or inthat they contain a novel image instead of one of the three familiarimages. Out of the 12 panels in the second set, four panels areidentical to panels shown in the first set, four panels contain afamiliar image in a novel location, and four panels contain a novelimage in the original position of a familiar image.

The participants were first read the instructions and shown an exampleof what was expected from them in this test. Then they were presentedwith the first set of 12 panels (reference set), one at a time, foreight seconds each and asked to memorize the images and their positions.Without delay, they were presented with the second set of 12 panels andwere prompted to identify each panel as being either identical to thecorresponding panel in the first set (“No Change score”), or containinga novel image (“Novel Image score”) or a novel location of a familiarimage (“Novel Location score”). Their answers provided the total NINLimmediate score, with a maximum of 12 points (“NINL I”). After fiveminutes (and without seeing the reference set again), participants werepresented with the second set and asked the same questions. Theseanswers provided the total NINL delayed score, with a maximum of 12points (“NINL II”). Test performance was analyzed using NINL I, NINL IIand three sub-scores for each. The Novel Location and Novel Imagesub-scores reflected the ability to correctly identify the exact NovelLocation and Novel Image, respectively (maximal four points each). TheNo Change sub-score reflected correct identification of panels identicalto those seen in the first set (maximal four points).

d. Spatial Span Forward (SSF) and Spatial Span Backward (SSB)

SSF and SSB are also part of the Wechsler Memory Scale III developed andpublished by the Psychological Corporation. They provide a nonverbalmeasure of immediate memory. Both tests involve a board containing tenrandomly anchored cubes. The participants were asked to watch theinvestigator tap the cubes (1 cube/second) in a prearranged order and toreproduce these tapping sequences. In the SSF test the tapping isperformed in order of the example (forward), while in the SSB test it isperformed in reverse order compared with the example (backward). Thedifficulty increases with the number of cubes tapped; from two cubes upto nine cubes. A correct sequence is scored as one point. Testing endedwhen the participants failed to reproduce the sequence correctly aftertwo trials or when the pair of nine cubes sequence was tapped correctly.

e. Memory Island

Memory Island, a computer-generated virtual reality (“VR”) worlddescribed in Section VI, was used in the second series of trials toassess spatial learning and memory requiring navigation. Briefly, theparticipants were immersed through a high quality 19-in. Dell computermonitor and a Harmon Kardon HK395 stereo speaker system with subwoofer.A Microsoft Sidewinder joystick was used to determine the direction andspeed of movement in the virtual world. The virtual world simulated anisland environment of 347×287 m² composed of four quadrants, eachcontaining a different target item.

The participants were first asked to navigate to a target locationvisibly marked with a flag adjacent to the target (visible targetsession). Targets in all four quadrants were used for visible targettraining in three trials. The starting orientation of the participantwas varied in each trial, and these variations were kept consistent forall participants, as the starting orientation for a particular trialinfluenced the difficulty level of that trial. After being trained tolocate the visible targets, the participants were trained to navigate toa hidden target (here, no flag adjacent to the target) in three trials.In this part of the test, the participants had to remember where thehidden target was and how to get there (hidden target session). Thelocation of the hidden target was kept constant for all participants(Seagull target). In each trial of the visible or hidden target session,if the participant was unable to locate the target within two minutes, adirectional arrow appeared to guide them to the target. Following thehidden target trials, the participant performed a 30 second probe trial(target removed). In each trial, movement of the participants wastracked and recorded in time-stamped coordinate files, which were usedto calculate total distance moved (feet), velocity (feet per second),latency (seconds), cumulative distance to the target (feet) andpercentage time spent in the target quadrant until it was located or upto 120 seconds, whichever came first. For the probe trial, thepercentage time spent in each quadrant was analyzed. Trials in which thetarget was located within two minutes were defined as successful trials.The percentage of successful trials in the visible and hidden targetsession was used as an additional measure of performance.

4. Salivary Cortisol and Testosterone

For assessment of salivary cortisol and testosterone levels and toassess potential correlations of these levels with measures of cognitiveperformance, samples of saliva were collected at the beginning of thesession. (See Table 3.) Salivary cortisol and testosterone levels weredetermined using commercial kits by the General Clinical Research Centerat Oregon Health Science University.

5. Statistical Analysis

As ε2 might provide protection against age-related cognitive decline,possible effects of ε2 on cognitive performance of the studyparticipants were assessed. As no such effects were found, the studyparticipants were divided in ε4 carriers and non-ε4 carriers. The lackof an effect of ε2 on cognitive performance of the study participantsmight have been caused by the relatively low number of ε2 carriers inthis sample. Because performance on immediate and delayed parts of thecognitive tests from the same subjects is likely to be correlated, amixed effects model (repeated measures design) was used to evaluate thechange of cognitive performance test for recall parts (Family Picturesand Faces). This model was also adopted for analyzing performance acrosstrials in Memory Island. Based on the Bayesian information criterion, acompound symmetrical structure as the variance-covariance matrix wasselected.

In addition, generalized estimating equation (“GEE”) regression for theobject recognition outcomes, such as Novel Image, Novel Location, NoChange, and Total NINL scores was used to obtain estimates clustered bystudy participant, and odds ratios were estimated for significantcomparisons. The method of GEE is often used to analyze longitudinal andother correlated response data.

When tests did not contain a delayed recall part, statisticaldifferences between ε4 and non-ε4-carrying groups as well as between menand women were determined using analyses of covariance (“ANCOVA”) with,in both cases, age as a covariate. Regarding the probe trial,Mann-Whitney U tests were conducted to assess effects of sex and ε4 ontime spent in the target quadrant. A Friedman test and when appropriatefollow-up pairwise comparisons using Wilcoxon signed rank-tests wereused to assess effects of sex and ε4 on the percentage of time spent ineach quadrant of Memory Island. To assess significance of linearcorrelations, Pearson correlation calculations with two-tailed p-valueswere used. To limit the risk of violation of a normal distribution,outliers were removed from testosterone, cortisol and reaction times.Adjustment for multiple testing was handled by using repeated-measuresanalyses. Age was used as a covariate in all analyses.

Statistical analyses were performed using SPSS software (SPSS version14.0: SPSS Inc., Chicago, Ill., USA) and Statistical Analysis Systemversion 9.1 (SAS Institute, Cary, N.C., USA).

B. Results of Second Series of Trials

1. Facial Recognition

Performance was slightly better on Faces II than Faces I (F=6.91,p=0.010). The estimated mean for the Faces II was 35.70, while theestimated mean for the Faces I was 33.95 with a standard error of 0.53.

2. Family Pictures

FIG. 10A is a chart showing, for elderly women and men, an effect of sexon “Family Pictures” test scores. There was strong evidence that women(estimated mean ±S.E.M., 34.58±1.30) performed better than men(estimated mean ±S.E.M., 23.84±2.09) in the Family Pictures test(F=22.52, p<0.001).

3. Object Recognition

FIG. 10B is a chart showing, for elderly women and men, an effect of ε4on NINL total scores (combined immediate and delayed scores). An effectwas identified for ε4 on NINL total scores (combined immediate anddelayed scores) (X²=4.23, p=0.040). At any given age, non-ε4 carriershad a higher estimated NINL total score than ε4 carriers. Mean NINLscores for non-ε4 and ε4 carriers were 9.01±0.25 and 8.10+0.39,respectively. There was a trial by ε4 interaction (X^(2=4.93), p=0.026,FIG. 7B); comparing the immediate and delayed object recognition testtotal scores, ε4 carriers showed a larger decline in performance thannon-ε4 carriers. With regard to the sub-scores, effects were identifiedfor sex and ε4 on the novel location sub-scores (combined immediate anddelayed scores). FIGS. 10C and 10D are charts showing, for elderly womenand men, effect of ε4 on Novel Location sub-scores (combined immediateand delayed scores), and effect of sex on Novel Location sub-scores,respectively. There was suggestive evidence of non-ε4 carriersperforming better than ε4 carriers (X^(2=4.01), p=0.045, FIG. 10C) andof women performing better than men (X^(2=4.82), p=0.028, FIG. 10D). Forthe no change sub-scores, interactive trial by ε4 effects were found(X^(2=7.64), p=0.006). In contrast to the novel location sub-scores, theanalyses of the novel image sub-scores yielded that performance waslower in the delayed score compared with the immediate recall (X²=5.30,p=0.021).

4. Spatial Span

No statistically significant effects were identified for sex (F=1.21,p=0.273) or ε4 (F=0.79, p=0.375) on SSF test performance. SSB testperformance showed no effects of ε4 (F=0.71, p=0.791), or effect of sex(F=1.60, p=0.209).

5. Memory Island

Spatial learning and memory requiring navigation were assessed using avirtual reality, spatial navigation (Memory Island) test. FIGS. 11A and11B are charts showing, for elderly women and men, effect of sex onvelocity during visible target trials, and effect of sex on velocityduring hidden target trials, respectively. FIGS. 12A-12F are chartsshowing, for elderly women and men, (A) effect of ε4 on velocity duringa visible target session, (B) effect of ε4 on velocity during a hiddentarget session, (C) effect of ε4 on cumulative distance during a visibletarget session, (D) effect of ε4 on cumulative distance during a hiddentarget session, (E) effect of ε4 on latency to reach target during avisible target session, and (F) effect of ε4 on latency to reach targetduring a hidden target session (P<0.05), respectively.

During the visible target trials, an effect was identified for sex(F=8.15, p=0.005, FIG. 11A) on velocity. Therefore, average velocityduring the visible target session was used as an additional covariate inall repeated-measures analyses of covariance (except for percentage timespent in the target quadrant as it is typically not related to speed ofmovement). Velocity also increased significantly across the visibletrials (F=3.58, p=0.036). Learning curves during the visible trials (asshown in FIGS. 12A, 12B, 12C and 12D) demonstrate that the participantsunderstood and could navigate in the three dimensional virtualenvironment. As shown in FIG. 12A, overall velocity increased acrosstrials during the visible target session (P<0.05). During the hiddentarget session, effects of ε4 were found on cumulative distance to thetarget (F=5.14, p=0.026, FIG. 12D) and on latency (F=6.17, p=0.015, FIG.12F). For example, ε4 carriers showed larger cumulative distance to thetarget at 120 seconds compared with non-ε4 carriers (P<0.05). Incontrast, no statistically significant effects were identified for ε4 oncumulative distance (FIG. 12C) or latency to the target during thevisible target session (FIG. 12E).

FIGS. 13A and 13B are charts showing, for elderly women and men testedwith a virtual reality, spatial navigation software tool in a probetrial (target removed), effect of sex on percentage of time spent infour quadrants, and effect of ε4 on percentage of time spent in fourquadrants, respectively. Women spent more time in the right quadrant(P<0.01) compared with the target quadrant and more time in the targetquadrant compared with the left (P<0.05) and to the opposite quadrant(P<0.01) while men spent more time in the target quadrant compared withthe left quadrant (P<0.05). Non-ε4 carriers spent more time in thetarget compared with the left quadrant (P<0.001) and the oppositequadrant (P<0.05) while ε4 carriers spent more time in the rightquadrant compared with the target quadrant (P<0.01).

In particular, women spent more time in the right quadrant than thetarget quadrant (z=−2.68, p=0.007), and more time in the target quadrantthan the left (z=−2.92, p=0.004) and opposite quadrant (z=−2.07,p=0.039), while men spent only more time in the target quadrant than theleft quadrant (z=−2.45, p=0.014) (FIG. 13A). Non-ε4 carriers spent moretime in the target quadrant than the left (z=−3.59, p<0.001) and theopposite quadrant (z=−2.54, p=0.011) while ε4 carriers spent more timein the right quadrant than the target quadrant (z=−2.74, p=0.006, FIG.13B). Comparatively, non-ε4 carriers spent more time in the targetquadrant (31%) than ε4 carriers (15%) (z=−2.03, p=0.042, FIG. 13B).

6. Salivary Cortisol and Testosterone Levels and Correlations withCognitive Performance

FIG. 14 is a chart showing, for elderly women and men, effect of ε4 onsalivary testosterone levels. Effects were identified for sex (F=3.944,p=0.05 and F=4.245, p=0.04 after controlling for age, FIG. 14) and ε4(F=4.520, p=0.04 and F=4.036, p=0.06 after controlling for age, FIG. 14)on salivary testosterone levels. Women carrying ε4 had lowertestosterone levels compared with non-carriers while men carrying ε4 hadhigher testosterone levels compared with non-carriers. Age was includedas a covariate in the analysis.

Based on this result and to avoid spurious conclusions, potentialcorrelations of salivary testosterone levels with cognitive measures inthe complete sample were not considered. Only in men, salivary cortisollevels correlated with some cognitive measures. (See Table 4.)

TABLE 4 Correlations of salivary cortisol levels with cognitivemeasures. Cortisol Level Measure Women Men Non ε4 ε4 MMSE^(a) −0.05−0.32 −0.05 −0.22 BAI 0.05 0.12 0.03 0.39 WRAT-R −0.08 0.03 −0.03 −0.14Reaction 0.16 −0.33 0.05 −0.10 times^(b) Spatial span^(c) SSF 0.00 0.270.03 0.03 SSB −0.03 −0.11 −0.11 0.05 Faces^(d) Faces immediate 0.04−0.22 0.03 −0.17 Faces delayed −0.11 −0.23 −0.04 −0.49^($) Family FamilyPictures −0.08 −0.07 0.05 −0.23 Pictures immediate Family Pictures −0.03−0.15 0.04 −0.15 delayed NINL^(a) No change −0.08 −0.06 −0.09 0.02immediate No change delayed −0.09 −0.02 −0.08 −0.07 Novel image −0.05−0.45* −0.12 −0.14 immediate Novel image −0.01 −0.37⁺ −0.07 −0.05delayed Novel Location −0.02 −0.28 0.06 −0.38 immediate Novel Location0.02 −0.12 0.08 −0.14 delayed Total NINL I −0.07 −0.004* −0.08 −0.31Total NINL II −0.03 −0.33 −0.04 −0.14 Memory Average velocity −0.05 0.10−0.03 −0.16 Island visible Total distance −0.21 0.36 −0.03 −0.30 target% Successful trials 0.11 0.05 0.07 0.20 session^(e) % In target quadrant0.20 −0.09 0.14 0.04 Cumulative distance −0.08 0.01 −0.06 0.03 MemoryAverage velocity −0.09 0.05 −0.13 0.00 Island Total distance 0.09 0.210.09 0.22 hidden target % Successful trials −0.18 −0.24 −0.19 −0.41session^(f) % In target quadrant −0.21 −0.11 −0.19 −0.30 Cumulativedistance 0.13 0.32 0.17 0.43 ^(a)N = 109; missing four cortisol samples,two outliers removed. ^(b)N = 107; two outliers removed. ^(c)N = 104;five participants dropped out of the study between the two testingtimes, one missing score. ^(d)N = 108; missing one score. ^(e)N = 103;six participants did not complete the visible target session. ^(f)N =80; 29 did not complete the hidden target session. *Correlation issignificant at the 0.05 level (two-tailed). ^($)While p < 0.05, closerexamination showed it was driven by a single datapoint (P = 0.187without). ⁺p = 0.051.

FIGS. 15A and 15B are charts showing, for elderly men, correlation ofsalivary cortisol levels with NINL I novel image recognition, andcorrelation of salivary cortisol levels with NINL II novel imagerecognition, respectively. In the object recognition tests, Novel Imagesub-scores correlated with salivary cortisol levels; immediate scores(r=−0.45, p=0.05, FIG. 12A), and delayed scores (r=−0.37, p=0.05, FIG.15B). Total NINL scores correlated also with salivary cortisol levels(r=−0.44, p=0.05). No such correlations were found in women.

TABLE 5 Effects of ε4 and sex on cognitive measures (repeated-measuresanalyses)^(a) Within Interaction ε4 Sex subject^(b) ε4 × Seq Sex × SeqMeasure N F P F P F P F P F P Faces^(c) 114 0.01 0.943 1.00 0.320 6.910.010 0.15 0.696 0.90 0.344 Family 115 2.50 0.117 22.57 <.0001 0.120.733 1.73 0.191 3.64 0.059 Pictures Visible 109 0.31 0.861 8.15 0.0053.58 0.036 1.82 0.170 2.11 0.131 Velocity Visible 109 0.77 0.781 3.240.075 0.50 0.535 0.11 0.818 0.61 0.486 Total Distance Visible 109 3.260.074 0.65 0.421 0.33 0.639 2.14 0.138 0.50 0.537 Latency Visible % 1091.07 0.303 3.73 0.056 2.18 0.117 0.93 0.392 0.69 0.496 Time spent inTarget Visible % 109 2.06 0.154 0.05 0.821 1.03 0.353 1.14 0.317 0.010.987 Successful trials Visible 109 0.18 0.670 0.55 0.462 0.34 0.6952.00 0.142 0.69 0.493 Cumulative Distance Hidden 84 2.37 0.128 3.400.069 2.89 0.059 0.10 0.905 1.91 0.151 Velocity Hidden 84 1.10 0.2971.00 0.321 0.02 0.983 0.84 0.433 1.12 0.331 Total Distance Hidden 846.17 0.015 0.69 0.410 1.01 0.366 0.83 0.438 2.40 0.094 Latency Hidden %84 3.48 0.066 0.76 0.385 1.33 0.268 1.25 0.288 1.18 0.311 Time spent inTarget Hidden % 84 2.06 0.154 0.05 0.821 1.03 0.353 1.14 0.317 0.010.987 Successful trials Hidden 84 5.14 0.026 0.62 0.433 0.73 0.482 0.210.809 2.60 0.078 Cumulative Distance ^(a)There were 19 models in Table5. Since there were 19 separate models for different outcomes (responsevariables), a multiple test procedure for controlling the family-wiseerror rate was not applied when testing general hypotheses defined interms of sub-models. Age was used as a covariate in all analyses.^(b)Repeated measures analyses were used to assess potentialwithin-subjects effects. ^(c)Faces represented a combined measure of“Faces I and Faces II.”

TABLE 6 Effects of ε4 and sex on cognitive measures (GEE regressionanalysis)^(a) Within Interaction ε4 Sex subject^(b) ε4 × Seq^(c) Sex ×Seq Measure N χ² P χ² P χ² P χ² P χ² P NINL 115 4.23 0.040 2.51 0.1133.48 0.062 4.93 0.026 0.09 0.767 total Novel 115 4.01 0.045 4.82 0.0280.09 0.763 1.82 0.178 0.52 0.472 location Novel 115 1.66 0.198 0.250.615 5.30 0.021 0.65 0.419 0.70 0.403 image No 115 0.00 0.968 0.130.723 0.00 0.980 7.64 0.006 1.44 0.230 change ^(a)Adjustment formultiple testing was handled using repeated-measures analyses. Age wasused as a covariate in all analyses. ^(b)Repeated-measures analyses wereused to assess potential within-subject effects. ^(c)Sequential, usedfor the assessments of potential interactions of ε4 or sex with therepeated measure of performance.

C. Discussion

The established cognitive tests Faces, Family Pictures, SSF and SSB wereadministered along with the object recognition and spatial navigationtests to non-demented elderly. Women performed better than men in thefamily pictures test (FIG. 10A). This sex difference in performance isconsistent with the better performance of women than men in recognizingpictures containing natural categories. Of the cognitive tests used,only the object recognition and spatial navigation tests were sensitiveto effects of ε4 on cognitive performance. The lack of ε4 effects in theestablished cognitive tests is consistent with other studies showing noeffects of ε4 on cognitive performance in the non-demented elderly.Deficits in episodic memory, memory for specific experiences that can bedefined in terms of time and space, are often the first symptomsexperienced by patients with AD. Therefore, poor performance on episodicmemory tests such as the object recognition and spatial navigation testscan indicate pre-clinical phases of cognitive impairment.

In the object recognition test, an effect was identified for ε4 (FIG.10C) on the novel location score (combined immediate and delayed score)with ε4 carriers performing worse on novel location, but not on novelobject, scores than non-ε4 carriers. These data indicate that it is moredifficult for ε4 carriers to accurately recall location. Together withthe poorer performance of ε4 than non-ε4 carriers on the spatialnavigation test, these data support the proposition that ε4 carriers areparticularly susceptible to spatial memory impairments.

In the object recognition test, there was also an effect of sex (FIG.10D) on the novel location score (combined immediate and delayed score)with women performing better than men, and there were no effects of sexon spatial navigation in the Memory Island test. Using the same objectrecognition test, for cognitive testing of healthy adult humans, no sexdifferences in performance were seen. In the first series of trials,using the same spatial navigation test used in the current study, adultmen performed better than adult women.

In the probe trial, elderly women and men did not spend most of theirtime in the target quadrant. (See FIG. 13A.) In contrast, younger adultwomen and men (20-44 years of age) did spend most of their time in thetarget quadrant in the probe trial. (Section VI.) These data areconsistent with the effect of age on spatial memory retention in young(25-45 years of age), middle-aged (45-65 years of age), and old (65-93years of age) humans shown in other studies, with the older studyparticipants showing lower measures of spatial memory retention in theprobe trial than the younger study participants. In the probe trial, ε4carriers spent more time in the right quadrant than the target quadrant(FIG. 13B). This was not seen in non-ε4 carriers. This is an importantfinding in that, based on the starting orientation in the probe trial,entering the right quadrant indicates that the study participantsnavigated straight rather than effectively searching for the targetlocation.

For salivary testosterone levels, there was a sex x ε4 interaction, withhigher salivary testosterone levels in ε4-carrying than non-ε4-carryingmen and lower salivary testosterone levels in ε4-carrying thannon-ε4-carrying women. (See FIG. 14.) In men, the difference betweenimmediate and delayed novel image recognition scores correlated withsalivary testosterone levels (r=0.473, p=0.015) and at each performancelevel difference between immediate and delayed recognition performance,ε4-carrying men had the highest salivary testosterone levels. Similarly,during the hidden target session of Memory Island, salivary testosteronelevels in male ε4 carriers correlated with latency (r=−0.92, p=0.03),cumulative distance to the target location (r=0.87, p=0.06), percentageof successful trials (r=0.85, p=0.07), and percentage of time spent inthe target quadrant (r=0.89, p=0.04).

The correlations of salivary cortisol levels with cognitive measureswere sensitive to sex. Only in men, salivary cortisol levels correlatedwith immediate total NINL scores, and immediate Novel Image sub-scores.(See Table 6.) The association of higher cortisol levels with poorercognitive performance in the elderly is consistent with other studies.However, the second series of trials underlines the importance ofconsidering ε4 and sex in assessing potential correlations of cortisolwith cognitive measures.

There was no significant sex difference in the proportion of ε4carriers. The lack of such a difference might have been due to the sexdifference in sample size with more women than men participating in thestudy, which in turn might relate to sex differences in longevity.

D. Conclusion

The object recognition and spatial navigation tests were sensitive toeffects of ε4 in the elderly. Differences in neuroanatomy, brain glucosemetabolism during mental activity, and brain activation in memory tasksbetween non-demented non-ε4 and ε4 carriers might contribute to theseeffects. As the mean age of the participants of this study was 82, thesedata indicate that while the ±4-associated risk to develop AD isage-dependent and maximal before this age, effects of ε4 on cognitiveperformance can be revealed in the old-old using episodic memory tests.

VIII. APOE ε4EFFECTS ON SPATIAL LEARNING AND MEMORY IN CHILDREN

This section describes example implementations as well as discussion ofspecific problems addressed and advantages for those implementations insome contexts. Alternatively, implementations of the precedingtechniques and tools vary in terms of technical details, specificadvantages and/or problems solved.

Compared to APOE ±3, APOE ε4 is a risk factor for age-related cognitivedecline and cognitive impairments following environmental challenges. Toassess whether APOE ε4 has effects on cognitive performance in children,they were given standardized cognitive tests as well as an objectrecognition test and a spatial navigation test sensitive to effects ofAPOE ε4 in the elderly. Children with APOE ε4 showed reduced novellocation recognition, reduced ability to navigate to a visible target,and reduced spatial memory retention. The early effect of APOE ε4 oncognition indicates predisposition to cognitive impairments later inlife.

Of the three major human APOE isoforms, which play roles in cholesterolmetabolism and are encoded by distinct alleles (ε2, ε3, and ε4), ε4increases the risk of age-related cognitive decline and cognitive injuryfollowing environmental insults. To assess potential effects of ε4 oncognition in children, 55 healthy 7-10 year-olds (girls: 17 non-ε4 andeight-ε4; boys: 24 non-ε4 and six-ε4) were given established cognitivetests, as well as an object recognition test and a spatial navigationtest requiring navigation sensitive to effects of ε4 in non-dementedelderly, and provided saliva samples for APOE genotyping and cortisollevels.

The inclusion criteria were healthy boys and girls 7-10 years of age.The exclusion criteria were children whose birth mother or legalguardian could not be interviewed, children with severe visualimpairments, children born more than 35 weeks premature, children withepilepsy, head injury, Tourette's syndrome, cerebral palsy, congenitalabnormalities, severe brain trauma, diagnosed with leukemia or any othermedical condition that could interfere with cognitive ability, orchildren exposed to elicit drugs during pregnancy. Children were frommiddle/upper class families and 84% Caucasian, 7% Hispanic, 3.5% AfricanAmerican, and 3.5% Pacific Islander. During pregnancy the average age ofthe mother's was 29 years old with 12.0% reporting smoking and 17.2%reporting at least some alcohol consumption during pregnancy.

Average cortisol levels did not correlate with any cognitive measure,but did correlate with anxiety scores (Pearson Correlation r=0.293,p<0.05). (See Table 7, below.) Thus, self-reported anxiety levels werelikely not a confounding factor for assessing cognitive measures. Eachchild was given cognitive tests during a 1.5 hr session. (See Table 7,below.)

TABLE 7 Cognitive measures in non-ε4 and ε4 carrying 7-10 year-old boysand girls. ANCOVA or REM Immediate Delay (Geno)¹ Task Non-ε4 ε4 carrierNon-ε4 ε4 carrier F p Dot 10.4 + 0.5 11.6 + 0.9 1.37 0.25 Location^(a) -Learning Dot Location - 11.01 + 0.5  12.4 + 0.9 1.95 0.17 Total ScoreDot Location - 11.6 + 0.3 11.8 + 0.6 0.05 0.81 Long Delay Dot Location -11.3 + 0.4 12.0 + 0.7 0.67 0.41 Short Delay CPT^(b) Non- 56.6 + 3.155.1 + 5.4 0.05 0.81 clinical score CPT % 49.6 + 2.2 51.8 + 3.8 0.240.62 Omissions CPT % 49.6 + 1.9 45.9 + 3.3 0.98 0.32 Commissions CPT HitRate 47.6 + 1.8 52.0 + 3.1 1.47 0.23 MASC^(c) - 45.5 + 2.3 47.4 + 3.90.17 0.67 Total Score WASI^(d) 56.9 + 1.8 57.3 + 3.1 0.01 0.91Vocabulary WASI Block 56.2 + 1.6 60.5 + 2.8 1.64 0.20 Design Family11.8 + 0.4 11.9 + 0.5 10.0 + 0.8  10.2 + 0.9  3.39 0.07 Pictures NINL -27.4 + 1.0 26.0 + 1.0 25.5 + 1.8  23.7 + 1.8  1.19 0.28 Total ScoreNINL -  5.1 + 0.4  4.2 + 0.5 5.2 + 0.7 4.8 + 0.8 0.10 0.74 Novel ImageNINL -  6.5 + 0.4  6.1 + 0.4 5.3 + 0.6 4.3 + 0.7 4.77^(e) 0.03^(e) NovelLocation Average  0.07 + 0.01  0.08 + 0.02 0.80 0.38 Cortisol levels(ug/dL) ^(a)Childrens' Memory Scale, Dot Location test. ^(b)Conners'Continuous Performance Test (“CPT”) is an attention test used in ADHDresearch and clinical assessments. ^(c)Multi-dimensional Anxiety Scalefor Children. ^(d)Wechsler Abbreviated Scale of Intelligence.^(e)Particularly significant effects

As attention (CPT, overall non-clinical score) correlated withperformance in most cognitive tests (p<0.05), it was included as acovariate in all analyses. Age was used as a covariate if age correctiontables were not available. The data were analyzed using ANCOVAs orrepeated measures (REM) with trial as the with-in subject variable andgenotype as the fixed factor. As no statistically significant effectswere identified for sex on any cognitive measure, boys and girls werecombined for the analysis. ε4 did not affect general cognitive abilityin children (Table 7), consistent with earlier reports. However, aneffect was identified for ε4 on novel location recognition (p<0.04) withlower delayed novel location recognition scores in ε4 carriers (p<0.03).As such an effect of ε4 was also seen in non-demented healthy elderly,novel location recognition can be used to detect effects of ε4throughout life.

Spatial learning and memory requiring navigation were assessed using thevirtual reality Memory Island test. FIGS. 16A, 16B and 16C show effectsof ε4 on Memory Island performance in 7-10 year-old boys and girls. FIG.16A shows effects of sex on velocity during the visible and hiddentarget trials (* p<0.04). FIG. 16B shows effects of ε4 on cumulativedistance to the target in the visible (*p<0.005), but not hidden, targettrials. FIG. 16C shows percentage of time spend in quadrants for ε4 andnon-ε4 children (* p<0.04 for the target quadrant versus all otherquadrants). Compared to the left and right quadrants, the childrentended to spend more time in the opposite quadrant. This might be due tothe fact that the start orientation in the probe trial faces theopposite quadrant.

The velocity of boys was higher than girls during the trials with thevisible (F=4.75; p<0.04) and hidden (REM ANCOVAs F=7.90; p<0.007)target. (See FIG. 16A.) Therefore, velocity was used as covariate forthe analyses of performance on Memory Island. Non-ε4 carriersoutperformed ε4 carriers during the trials with the visible (F=4.53;p<0.04), but not hidden (F=0.11; p=0.74), target by navigating closer tothe visible target location. (See FIG. 16B.)

Fifteen minutes following the last hidden trial, non-ε4 carriers showedspatial memory retention in the probe trial (no target present) andsearched most of the time in the quadrant previously containing thetarget p<0.04) but ε4 carriers did not. (See FIG. 16C.) Similarly, while75.6% of non-ε4 carriers showed target preference, only 43% of ε4carriers did (p<0.04, Fisher's exact test).

Thus, effects of ε4 on spatial learning and memory are already detectedin 7-10 year-old children and indicate predisposition to cognitiveinjury following environmental challenges and/or age-related cognitivedecline.

IX. COGNITIVE STATUS ASSESSMENT AND THERAPEUTIC INTERVENTION EXAMPLESExample 1

In this example, the cognitive status of an elderly subject suspected tohave dementia (e.g., an age-associated dementia, such as Alzheimer'sdisease) is assessed. The subject is given the Memory Island and NINLtests described herein. The subject's performance on the Memory Islandtest (e.g., distance traversed, time elapsed before reaching target,percentage of successful trials, time spent in target area, velocity ofmovement) and NINL tests (e.g., Novel Location score, Novel Image score,No Change score, total NINL score) is compared with previously measuredperformance benchmarks for non-demented individuals of the same sex andsimilar age.

Example 2

In this example, the cognitive status of an elderly subject suspected tohave age-associated dementia is assessed over time. The subject isinitially given Memory Island and NINL tests described herein. Thesubject's performance on the Memory Island and NINL tests is comparedwith previously measured performance benchmarks for non-dementedindividuals of the same sex and similar age. One year later, the subjectis given a second round of Memory Island and NINL tests. The subject'sperformance on the Memory Island and NINL tests is compared with thesubject's own performance the previous year.

Example 3

In this example, the cognitive status of a child subject suspected tohave a genetic predisposition toward dementia later in life (for examplean age-associated dementia, such as Alzheimer's disease) is assessed.The child subject is given the Memory Island and NINL tests describedherein. The child subject's performance on the Memory Island and NINLtests is compared with previously measured performance benchmarks fornon-demented individuals of the same sex and similar age.

Example 4

In this example, a subject who has been diagnosed with dementia (forexample an age-associated dementia, such as Alzheimer's disease) isgiven the Memory Island and NINL tests described herein. Baselinemeasures are determined (for Memory Island: distance traversed, timeelapsed before reaching target, percentage of successful trials, timespent in target area, velocity of movement; for NINL: Novel Locationscore, Novel Image score, No Change score, total NINL score), and thesubject is given an anti-Alzheimer's drug (such as Donepezil (ARICEPT)or anti-APOE ε4 drug) as a therapeutic intervention.

For a subject given Donepezil, the subject is treated with 5 mg per dayof the drug for a period of at least four to six weeks, and then thesubject is once again given the Memory Island and NINL tests. If adesired improvement in scores is not obtained, then the dose of the drugis increased to 10 mg per day. Testing with the Memory Island and NINLtests is then repeated to assess the response to the drug, where achange in test scores indicates a response to the drug.

Example 5

In this example, a subject who has been diagnosed with dementia (forexample an age-associated dementia, such as Alzheimer's disease) isgiven the Memory Island and NINL tests described herein. Baselinemeasures are determined as described in Example 4, and the subject isgiven an anti-Alzheimer's drug (such as Donepezil (ARICEPT) or anti-APOEε4 drug) as a therapeutic intervention.

For a subject given Donepezil, the subject is treated with 5 mg per dayof the drug for a period of at least four to six weeks, and then thesubject is once again given the Memory Island and NINL tests. If adesired improvement in scores is not obtained, the subject is given adifferent drug. Testing with the Memory Island and NINL tests is thenrepeated to assess the response to the different drug, where a change intest scores indicates a response to the different drug.

Having described and illustrated the principles of our invention withreference to various described embodiments, it will be recognized thatthe described embodiments can be modified in arrangement and detailwithout departing from such principles. It should be understood that theprograms, processes, or methods described herein are not related orlimited to any particular type of computing or clinical environment,unless indicated otherwise. Various types of general purpose orspecialized computing environments may be used with or performoperations in accordance with the teachings described herein. Elementsof the described embodiments shown in software may be implemented inhardware and vice versa.

In view of the many possible embodiments to which the principles of ourinvention may be applied, we claim as our invention all such embodimentsas may come within the scope and spirit of the following claims andequivalents thereto.

1. A method comprising: receiving input from a user as the userinteracts with software presenting a virtual reality environment thatincludes a first-person, three-dimensional graphical rendering of thevirtual reality environment; measuring performance of the user in thevirtual reality environment based at least in part upon the receivedinput; and using the measured performance of the user in the virtualreality environment in analysis of cognitive status; wherein themeasured performance includes one or more of distance traversed in thevirtual reality environment, time elapsed before reaching a target inthe virtual reality environment, percentage of successful trials, timespent in a target area of the virtual reality environment, and velocityof movement in the virtual reality environment.
 2. The method of claim1, wherein the virtual reality environment is organized in a coordinatespace, and wherein the measuring comprises tracking position of the userover time in the coordinate space.
 3. The method of claim 2, wherein afile includes results of the tracking as time-stamped coordinatelocations.
 4. The method of claim 1, wherein the virtual realityenvironment includes multiple areas, and wherein the measuredperformance further includes pattern of movement between the multipleareas.
 5. The method of claim 1, wherein the virtual reality environmentincludes multiple areas, and wherein the measured performance furtherincludes pattern of time spent in respective areas of the multipleareas.
 6. The method of claim 1, wherein the distance traversed is totaldistance or cumulative distance.
 7. The method of claim 1, furthercomprising: presenting the user with one or more visual or audible cuesas to location of the target in the virtual reality environment, whereinthe performance of the user is measured in conjunction with presentationof the cues, and wherein the performance of the user is subsequentlymeasured without presentation of the cues to the user.
 8. The method ofclaim 1, further comprising: measuring neural activity of the user witha magnetic resonance imaging tool as the user interacts with thesoftware presenting the virtual reality environment, wherein theanalysis of cognitive status is based at least in part on the measuredneural activity.
 9. The method of claim 1, further comprising: measuringneural activity of the user with a functional magnetic resonance imagingtool as the user interacts with the software presenting the virtualreality environment, wherein the analysis of cognitive status is basedat least in part on the measured neural activity.
 10. The method ofclaim 1, wherein a personal computer system presents the virtual realityenvironment.
 11. The method of claim 10, wherein a server computersystem provides the virtual reality environment to the personal computersystem over a network connection.
 12. The method of claim 1, wherein thevirtual reality environment is graphically rendered to the user usingvirtual reality goggles.
 13. The method of claim 1, wherein the inputincludes direction input and speed input received from a joystick. 14.The method of claim 1, wherein the analysis of cognitive status is usedto detect presence or extent of age-related cognitive decline.
 15. Themethod of claim 14, wherein the age-related cognitive decline is adecline in memory performance or learning performance.
 16. The method ofclaim 1, wherein the analysis of cognitive status is used to detectpresence or extent of pediatric cognitive disability.
 17. The method ofclaim 16, wherein the pediatric cognitive disability is a memoryperformance problem or learning performance problem.
 18. The method ofclaim 1, wherein the analysis of cognitive status is used to detectpresence or extent of progression of Alzheimer's disease.
 19. The methodof claim 1, wherein the analysis of cognitive status is used to detectpresence of a characteristic of pre-clinical Alzheimer's disease. 20.The method of claim 1, wherein the analysis of cognitive status is usedto measure responsiveness of the user to treatment of Alzheimer'sdisease.
 21. The method of claim 1, further comprising: providing atreatment regimen for the user based at least in part on the assessedcognitive status.
 22. The method of claim 1 further comprising:comparing the analysis of cognitive status to one or more prior analysesof cognitive status to assess change in cognitive status of the user.23. The method of claim 1, wherein the analysis comprises adjusting foreffects of sex and/or age on the measured performance.
 24. The method ofclaim 1, wherein the analysis comprises adjusting for effects oflearning of navigation skills in the virtual reality environment on themeasured performance.
 25. The method of claim 1, wherein the analysiscomprises adjusting for effects of learning of landscape of the virtualreality environment on the measured performance.
 26. The method of claim1, wherein analysis of cognitive status comprises analysis of a responseof the user to a therapeutic intervention to treat cognitive decline.27. The method of claim 26, wherein the therapeutic interventioncomprises a drug therapy.
 28. The method of claim 26, wherein theanalysis of a response of the user further comprises determining atherapeutic dose of the drug therapy.
 29. The method of claim 26,wherein the therapeutic intervention is a proposed intervention that isbeing studied for efficacy.
 30. The method of claim 26, wherein thetherapeutic intervention is an APOE ε3 or APOE ε2 mimetic, acholinesterase inhibitor, an N-methyl-aspartate receptor antagonist, ahormone therapy, or a vitamin.
 31. The method of claim 1 wherein theanalysis is performed by a software tool that includes patternrecognition or other artificial intelligence aspects.
 32. The method ofclaim 1 wherein the analysis and the measuring performance are performedby an integrated software tool.
 33. The method of claim 1 whereindiminished cognitive status is indicated by one or more of increaseddistance traversed in the virtual reality environment, increased timeelapsed before reaching a target in the virtual reality environment,decreased percentage of successful trials, decreased time spent in atarget area of the virtual reality environment, and decreased velocityof movement in the virtual reality environment.
 34. A method comprising:receiving input from a user as the user interacts with softwarepresenting a virtual reality environment that includes a first-person,three-dimensional graphical rendering of the virtual realityenvironment; in a first round, measuring performance of the user innavigating in the virtual reality environment to a target indicated byat least a first visual cue in the virtual reality environment based atleast in part upon the received input; in a second round that lacks theat least first visual cue, measuring performance of the user innavigating to the target in the virtual reality environment based atleast in part upon the received input; using the measured performance ofthe user in the virtual reality environment for the first and secondrounds in analysis of cognitive status.
 35. A method comprising ofcorrelating genotype with cognitive status, the method comprising:receiving input from a user known to have a first genotype as the userinteracts with software presenting a virtual reality environment thatincludes a first-person, three-dimensional graphical rendering of thevirtual reality environment; measuring performance of the user innavigating to a target in the virtual reality environment based at leastin part upon the received input; using the measured performance inanalysis of cognitive status for the user known to have the firstgenotype, wherein the analysis comprises a comparison of the measuredperformance of the user in the virtual reality environment relative tomeasured performances for a user group for the first genotype.
 36. Themethod of claim 35 wherein the first genotype is an APOE genotype.